contribution n. 1.貢獻(xiàn),贈送;捐贈,捐助。 2.投稿,來稿。 3.捐款,獻(xiàn)金;獻(xiàn)品,補(bǔ)助品。 4.【軍事】(向占領(lǐng)地人民征收的)軍稅;【法律】分擔(dān)(額)。 lay under contribution 強(qiáng)制派捐,勒派軍稅。 make a contribution to [towards] 捐贈;貢獻(xiàn)給。
variance n. 1.變化,變動,變更;變度,變量;【統(tǒng)計】(平)方(偏)差。 2.(意見等的)相異;不和,沖突,爭論。 3.【法律】訴狀和供詞的不符。 at variance with 和…不和;和…不符 (at variance with the facts 不符事實。His conduct is at variance with his words. 他言行不符)。 set at variance 使不睦,離間。
Regarding the variance contribution rate of each factor as right count weight , this thesis gets the evaluation score and rank 在計算綜合得分時是以因子的方差貢獻(xiàn)率為權(quán)數(shù)加權(quán)求和,由此得到各地區(qū)的綜合得分以及排名。
Spca arithmetic has smaller number of spectral principal components and greater variance contribution than pca by choosing proper kernel functions and parameters 當(dāng)選取合適的核函數(shù)和參數(shù)時,譜主成分的個數(shù)比主成分的個數(shù)要小且累積方差貢獻(xiàn)率要大。
The results of numerical calculations show that : the number of spectral principal component and cumulate variance contribution are different its depending on kernel functions 通過數(shù)值例子計算表明:取不同核函數(shù)而得到的譜主成分分析,其譜主成分的個數(shù)及累積方差貢獻(xiàn)率是有差別的。
Via numeric sample analysis , it is found that evaluation functions are constructed by weighing principal components for pca . however , evaluation functions can be quite different when there are more than three principal components and characteristic vectors other than first one are chosen in different directions . for spca , variance contribution can be greater than 90 % by selecting just one principle component 將譜主成分分析應(yīng)用于多指標(biāo)評價系統(tǒng)中,通過數(shù)值例子分析:主成分分析是通過對各個主成分加權(quán)構(gòu)造評價函數(shù),當(dāng)主成分個數(shù)不小三個時,從第二個特征向量開始,對方向的不同選取,可導(dǎo)致評價函數(shù)的極大差異:而用譜主成分分析,能做到只取一個譜主成分就可使方差貢獻(xiàn)率大于90 。
Therefore , spca gotten via selecting polynomial kernel functions is more accurate than pca in multi - index evaluation system , and has fewer dimensions . comparatively , for spca using gauss kernel function and laplace kernel function , it is required to normalize original data according to the category , and the constructed evaluation functions are better than the ones constructed via using pca . however , if variance contribution of first principal component of pca is more than 85 % , evaluation function curves of spca and pca are similar 在作多指標(biāo)評價中,選用多項式核函數(shù)而得到的譜主成分分析,比主成分分析得到的主成分具有維數(shù)低且精度高的優(yōu)點;而用gauss核函數(shù)和laplace核函數(shù)的譜主成分分析,需對原數(shù)據(jù)作同類別數(shù)據(jù)間的規(guī)范化,其構(gòu)造的評價函數(shù)也優(yōu)于用主成分方法構(gòu)造的評價函數(shù)。